Senser, an Israeli AIOps platform, has launched its AI-enhanced observability platform and secured $9.5 million in a seed funding round. The funding is led by Eclipse, with participation from Amdocs and other private investors. Senser’s platform utilizes machine learning and eBPF technology to help developers and operations teams identify the root causes of outages and service degradations in real-time.
Senser, an AIOps platform, has launched an AI-enhanced observability platform and secured $9.5 million in seed funding. Leveraging eBPF technology and machine learning, Senser aims to provide contextualized insights and reduce alert fatigue for operations teams. The platform offers a comprehensive infrastructure map and emphasizes explainability in its alerts, enhancing troubleshooting capabilities.
Enhancing Observability with AI and eBPF Technology
Senser differentiates itself by leveraging eBPF technology, which runs inside the Linux kernel and provides deep visibility into a company’s infrastructure without significant additional overhead. This enables Senser to monitor networking and application traffic more effectively. By combining eBPF with AI, Senser aims to deliver contextualized data to aid DevOps and site reliability teams in their troubleshooting efforts.
While many observability companies rely on dashboards to present data, Senser goes beyond this approach. The platform offers a comprehensive map of the organization’s infrastructure, including virtual machines, Kubernetes clusters, and microservices. It allows users to drill down to specific details, with a primary focus on production environments.
Addressing Challenges in Operational Efficiency
Senser’s co-founders, Amir Krayden, Yuval Lev, and Or Sadeh, shared a common background in the Israel Defense Forces and previously worked in a networking company before starting Senser. Their first-hand experience with complex systems and limited tools to debug them led them to create a solution that simplifies the lives of operations teams.
The platform’s goal is to reduce alert fatigue by delivering meaningful alerts based on system events rather than individual symptoms. Senser aims to provide explainable insights, enabling users to understand why an alert was triggered and what exactly went wrong. Combining eBPF and machine learning, Senser ensures that industrial applications, such as warehouse automation and robotics, run reliably and deliver insights to improve performance.
Senser’s Rapid Growth and Marketing Focus
Senser is experiencing rapid growth and currently employs 17 people. Notably, the company is already expanding its marketing team, indicating a strategic focus on storytelling and early visibility in the market. As CEO Amir Krayden emphasized, marketing is becoming increasingly important to effectively communicate the value and capabilities of their platform.